def _alignData(self):
"""Re-calibrate data using theoretical envelope."""
# split data to raster and intensities
xAxis, yAxis = numpy.hsplit(self.data, 2)
raster = xAxis.flatten()
intensities = yAxis.flatten()
# model profiles
models, exchanged = self._makeModels(raster)
# fit current data
fit = self._leastSquare(intensities, models, iterLimit=len(self.models))
# get all isotopes for all used models
isotopes = []
for i, abundance in enumerate(fit):
pattern = self.models[exchanged[i]][1]
isotopes += [(p[0], p[1]*abundance) for p in pattern]
# check isotopes
if not isotopes:
return
# make total envelope
profile = mod_pattern.profile(isotopes, fwhm=self.fwhm, points=10, model=self.peakShape)
# label peaks in profile
peaklist = mod_peakpicking.labelscan(profile, pickingHeight=0.95, relThreshold=0.01)
# find useful calibrants
calibrants = []
tolerance = self.fwhm/1.5
for peak in peaklist:
for point in self.data:
error = point[0] - peak.mz
if abs(error) <= tolerance:
if calibrants and calibrants[-1][0] == peak.mz and calibrants[-1][1] < point[1]:
calibrants[-1] = (point[0], peak.mz)
else:
calibrants.append((point[0], peak.mz))
elif error > tolerance:
break
# calc calibration
if len(calibrants) > 3:
model, params, chi = mod_calibration.calibration(calibrants, model='quadratic')
elif len(calibrants) > 1:
model, params, chi = mod_calibration.calibration(calibrants, model='linear')
else:
return
# apply calibration to data
for x in range(len(self.data)):
self.data[x][0] = model(params, self.data[x][0])
for x in range(len(self.spectrum)):
self.spectrum[x][0] = model(params, self.spectrum[x][0])
def envelope(self, points=10):
"""Make envelope for current composition."""
# get isotopes
isotopes = []
for x in self.models:
abundance = self.models[x][2]
isotopes += [(p[0], p[1]*abundance) for p in self.models[x][1]]
# make profile from isotopes
profile = mod_pattern.profile(isotopes, fwhm=self.fwhm, points=points, model=self.peakShape)
return profile
def envelope(self, points=10):
"""Make envelope for current composition."""
# get isotopes
isotopes = []
for x in self.models:
abundance = self.models[x][2]
isotopes += [(p[0], p[1] * abundance) for p in self.models[x][1]]
# make profile from isotopes
profile = mod_pattern.profile(isotopes,
fwhm=self.fwhm,
points=points,
model=self.peakShape)
return profile
def _makeModels(self, raster, reset=True):
"""Calculate pattern for every model."""
models = []
exchanged = []
# get raster
rasterMin = raster[0] - self.fwhm
rasterMax = raster[-1] + self.fwhm
for x in sorted(self.models.keys()):
CHECK_FORCE_QUIT()
# get compound
compound = self.models[x][0]
# check if mz is within raster
mz = compound.mz(self.charge)
if mz[0] > rasterMax or mz[1] < rasterMin:
continue
# calculate isotopic pattern
pattern = self.models[x][1]
if reset or pattern == []:
pattern = compound.pattern(fwhm=self.fwhm,
charge=self.charge,
real=False)
self.models[x][1] = pattern
# calculate model profile
profile = mod_pattern.profile(pattern,
fwhm=self.fwhm,
raster=raster,
model=self.peakShape)
model = profile[:, 1].flatten()
# check model profile
if model.any():
models.append(model)
exchanged.append(x)
# make models matrix
models = numpy.array(models)
return models, exchanged
def _makeModels(self, raster, reset=True):
"""Calculate pattern for every model."""
models = []
exchanged = []
# get raster
rasterMin = raster[0] - self.fwhm
rasterMax = raster[-1] + self.fwhm
for x in sorted(self.models.keys()):
CHECK_FORCE_QUIT()
# get compound
compound = self.models[x][0]
# check if mz is within raster
mz = compound.mz(self.charge)
if mz[0] > rasterMax or mz[1] < rasterMin:
continue
# calculate isotopic pattern
pattern = self.models[x][1]
if reset or pattern == []:
pattern = compound.pattern(fwhm=self.fwhm, charge=self.charge, real=False)
self.models[x][1] = pattern
# calculate model profile
profile = mod_pattern.profile(pattern, fwhm=self.fwhm, raster=raster, model=self.peakShape)
model = profile[:,1].flatten()
# check model profile
if model.any():
models.append(model)
exchanged.append(x)
# make models matrix
models = numpy.array(models)
return models, exchanged
def _alignData(self):
"""Re-calibrate data using theoretical envelope."""
# split data to raster and intensities
xAxis, yAxis = numpy.hsplit(self.data, 2)
raster = xAxis.flatten()
intensities = yAxis.flatten()
# model profiles
models, exchanged = self._makeModels(raster)
# fit current data
fit = self._leastSquare(intensities,
models,
iterLimit=len(self.models))
# get all isotopes for all used models
isotopes = []
for i, abundance in enumerate(fit):
pattern = self.models[exchanged[i]][1]
isotopes += [(p[0], p[1] * abundance) for p in pattern]
# check isotopes
if not isotopes:
return
# make total envelope
profile = mod_pattern.profile(isotopes,
fwhm=self.fwhm,
points=10,
model=self.peakShape)
# label peaks in profile
peaklist = mod_peakpicking.labelscan(profile,
pickingHeight=0.95,
relThreshold=0.01)
# find useful calibrants
calibrants = []
tolerance = self.fwhm / 1.5
for peak in peaklist:
for point in self.data:
error = point[0] - peak.mz
if abs(error) <= tolerance:
if calibrants and calibrants[-1][
0] == peak.mz and calibrants[-1][1] < point[1]:
calibrants[-1] = (point[0], peak.mz)
else:
calibrants.append((point[0], peak.mz))
elif error > tolerance:
break
# calc calibration
if len(calibrants) > 3:
model, params, chi = mod_calibration.calibration(calibrants,
model='quadratic')
elif len(calibrants) > 1:
model, params, chi = mod_calibration.calibration(calibrants,
model='linear')
else:
return
# apply calibration to data
for x in range(len(self.data)):
self.data[x][0] = model(params, self.data[x][0])
for x in range(len(self.spectrum)):
self.spectrum[x][0] = model(params, self.spectrum[x][0])
